US7085409B2 - Method and apparatus for synthesizing new video and/or still imagery from a collection of real video and/or still imagery - Google Patents
Method and apparatus for synthesizing new video and/or still imagery from a collection of real video and/or still imagery Download PDFInfo
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- the present invention is directed toward the domain of image processing, in particular toward the creation of virtual images seen from arbitrary viewpoints from at least two real images.
- site models For general modeling of static scenes, site models provide a viable option. However, site models do not include appearance representations that capture the current and changing appearance of the scene. The dynamic components of a scene cannot, by definition, be modeled once and for all. Even for the static parts, the appearance of the scene changes due to varying illumination and shadows, and through modifications to the environment. For maintaining up-to-date appearance of the static parts of the scene, videos provide a cost-effective and viable source of current information about the scene.
- Image-based modeling and rendering as set forth in “Plenoptic Modeling: An Image-Based Rendering System” by L. McMillan and G. Bishop in SIGGRAPH 1995, has emerged as a new framework for thinking about scene modeling and rendering.
- Image-based representations and rendering potentially provide a mix of high quality rendering with relatively scene independent computational complexity.
- Image-based rendering techniques may be especially suitable for applications such as tele-presence, where there may not be a need to cover the complete volume of views in a scene at the same time, but only to provide coverage from a certain number of viewpoints within a small volume. Because the complexity of image-based rendering is of the order of the number of pixels rendered in a novel view, scene complexity does not have a significant effect on the computations.
- multiple cameras are used to capture views of the dynamic object.
- the multiple views are synchronized at any given time instant and are updated continuously.
- the goal is to provide 360 degrees coverage around the object at every time instant from any of the virtual viewpoints within a reasonable range around the object.
- zoom lenses and cameras In order to provide control of zoom for many users at the same time, it is not feasible to use zoom lenses and cameras. Physical control of zoom through zoom lenses can be done for only one viewpoint at a time, and only by one user. Synthetic control of resolution based on real data can provide a limited control of resolution. Typically, such a control may be able to provide at least 2 33 magnification without appreciable loss of quality.
- the present invention is embodied in an image-based tele-presence system, in which images are forward warped using local depth maps and then merged to form high quality virtual images.
- this system uses an improved method to create a high quality virtual image, in real-time, as seen from a virtual viewpoint within a scene covered by a plurality of fixed electronic cameras.
- the selected images are used to create depth maps corresponding to these images.
- warp parameters are calculated to warp the selected images to the virtual viewpoint using their corresponding depth maps and the images are then warped using these sets of warp parameters.
- the warped images are merged to create the high quality virtual image as seen from the selected viewpoint.
- the system employs a color segmentation method to improve the quality and speed of local depth map calculations, particularly in textureless regions.
- the images are divided into segments of similar color (based on pixel values, or the combination of sub-pixel values) and an initial estimate depth of each segment is made.
- the depth is refined over a number of iterations in which the depth of each segment is optimized in turn, while holding the depth of the other segments fixed.
- the system employs a video blanket array of electronic cameras.
- This video blanket helps both optimize the number of cameras.
- a plurality of cameras are deployed in a geometric pattern on a surface.
- FIG. 1 is a schematic diagram demonstrating a hexagonal configuration of cameras used to provide efficient coverage of a scene.
- FIG. 2 is a flowchart of the method of this invention to create high quality virtual images.
- FIG. 3 is a flowchart of an exemplary method to create local depth maps using color segmentation.
- FIG. 4 is a pair of drawings illustrating a scene to demonstrate color segmentation.
- FIGS. 5 a , 5 b , and 5 c are graphs illustrating the iterative planarization process.
- FIGS. 6 a , 6 b , 6 c , and 6 d are graphs illustrating fast hypothesis testing of hypothesized color segmentation depth maps.
- FIG. 7 a is a diagram that illustrates an occlusion compatible traversal order method for warping of images in which the epipolar geometry is known.
- FIG. 7 b is a diagram that indicates the two regions which determine the appropriate traversal order to be followed using the rows and columns of the image as guidelines.
- FIG. 7 c is a diagram that demonstrates the 4-sheet horizontal-vertical traversal order where the epipole is a focus of contraction in region 50 of FIG. 7 b.
- FIG. 7 d is a diagram that demonstrates the 4-sheet vertical-horizontal traversal order where the epipole is a focus of contraction in region 51 shown in FIG. 7 b.
- FIG. 8 is an image diagram that is useful for describing a mesh based splatting process used to improve the quality of warped images.
- FIG. 9 is a top-plan view of a space to be imaged showing multiple cameras being used to capture images of a scene from a variety of angles.
- FIG. 10 is a block diagram of the virtual camera system of the present invention.
- FIG. 11 is a series of drawings demonstrating the steps of image processing employed in the present invention to create a high quality virtual image, from two selected real images.
- the present invention overcomes many of the problems of previous approaches to interactive tele-presence. This is accomplished using a new image-based approach.
- the limitations of tradition optical flow based tweening can largely be overcome by capturing the scene in 3D and then rendering the scene from novel viewpoints.
- the key issue for real-time implementation of prior art global 3D representations is their requirement for centralized processing of all the views at each time instant.
- Improved algorithms and camera placement allow the present invention to circumvent these problems and attain a viable interactive tele-presence.
- remote tele-presence One important aspect of remote tele-presence is the ability to provide continuously changing viewpoints of the remote environment and, thus, provide a sense of stability and natural movement within the environment.
- the viewpoints cover all the parts of the remote workspace that are relevant for a given application. For example, for a business meeting, it is natural to provide those views of the workspace/participants that a physically present human observer would see while sitting down or moving around.
- global panoramic and immersive views can be provided to show the complete environment.
- the ability to zoom-in on parts of the scene is also provided. Smooth transitions mimic the way humans explore a physical scene.
- An important aspect of such a tele-presence system is its unique feature of providing numerous users independent control over the view parameters within a wide range of viewpoints as well as camera zoom factors.
- an array of cameras is used as a video blanket to provide the desired scene coverage.
- the cameras are arranged in an optimal way to maximize coverage while minimizing the overall number of pixels that need to be processed.
- Depth/parallax maps between the cameras are computed with the assistance of a color segmentation method, which is used to generate local shape maps.
- the local shape maps along with the resolution image data are then used to provide multiple resolution renderings not only from the viewpoints of the real cameras but also for virtual cameras located within the range of the real cameras.
- planar, tubular, and spheroidal surfaces are not necessarily closed.
- Planar and tubular configurations both have cameras placed in a hexagonal pattern, i.e. at the vertices of hexagons which are tiled over the surface.
- cameras may be placed as the carbon atoms in a fullerene are, at the vertices of an array of pentagons and hexagons which are tiled over the surface.
- An algorithm for creating reliable local depth maps has been developed. Given relative poses and intrinsic parameters of multiple cameras in general positions and the corresponding positions of all scene points in the captured images, the 3D structure of a static scene can be recovered using a method called triangulation. Stereopsis is a non-invasive technique to achieve this goal through establishing image feature correspondence (correspondence matching) by machine analysis.
- the inventors use a 3D-reconstruction algorithm that employs a view-based volumetric representation of the scene.
- a convenient method to produce the correspondence matching is to use optical flow.
- Large displacements or in general large disparities between pairs of cameras can not be handled by the standard optical flow algorithms, however, because such displacements may not be within the capture range of gradient or search based methods.
- the inventors have designed a multi-hypothesis optical flow/parallax estimation algorithm that combines features of large range search and high precision of coarse-to-fine gradient methods.
- the algorithm starts with a set of hypotheses of fixed disparity. Estimates of flow at each point are refined with respect to each of the hypotheses. The final optical flow is generated by selecting the best flow at each point.
- new close-by views can be rendered using forward warping algorithms.
- a mesh-based super-resolution algorithm has been implemented. Using the correct occlusion compatible traversal order, the warped images demonstrate convincing 3D effects.
- the four main components of this algorithm are flow rendering from depth image, depth transformation, mesh-based super resolution warping, and occlusion compatible traversal. Finally, the warped images from two or more cameras are merged to further improve image quality, particularly to improve handling of occlusion/deocclusions.
- Exemplary modes of operation for such tele-presence systems are:
- Observation Mode Individuals use a tele-presence system to observe a remote location but without interacting with people or things at that location—to see, but be unseen. The user feels as if he is present in the scene without physically being there.
- Applications include:
- Guards (virtually and unobtrusively) walk around a facility, to observe activity of authorized personnel and check out possible intruders.
- Conversation Mode Two or more individuals at different locations use a tele-presence system to converse with one another.
- the tele-presence system provides face to face visual contact comparable to a physical meeting. It allows individuals to make eye contact, see who is addressing whom, or see who is paying attention and who is looking away.
- Applications include:
- Interaction Mode Two or more individuals use tele-presence to observe one another as they perform some common task.
- the system provides both observation and conversation capabilities—it allows one individual to observe what another is doing with his or her hands, while carrying on a conversation.
- Applications include:
- War room Military leaders at disparate locations share a (virtual) work environment, including workstation and wall displays showing mission activity, and walk around and talk to one another as if they were in the same room.
- Tele-conferencing Business people at remote locations take part in a meeting that includes common (virtual) work areas, such as white boards and viewgraph displays, while they walk around and talk to one another as if in the same room.
- common (virtual) work areas such as white boards and viewgraph displays
- Operating room A team of surgeons at disparate locations conducts an operation. Surgeons in the real operating room perform actual surgical steps, while surgeons at remote locations observe the patient in detail, provide advice, and demonstrate procedures in a virtual work-space.
- Kitchen Friends share a recipe and demonstrate preparation steps while each is in his or her own kitchen. They watch each other's hands, and look in the pots, and talk as if they were in the same room.
- Sports Friends at their own homes join one another in a virtual common arena to enjoy a broadcast sports event, viewing the event as if they were on the playing field.
- the present invention focuses on how dynamic images taken of complex objects such as human subjects at close range can be captured to create a representation that is amenable to real-time rendering that covers wide range of views of the subject. To realize this goal, advances have been made in three areas:
- FIG. 1 demonstrates an exemplary embodiment of a video blanket deployed on a planar surface to provide scene coverage by the cameras.
- FIG. 1 is a schematic diagram demonstrating a hexagonal configuration of cameras 1 used to provide efficient coverage of a scene.
- the horizontal elongation of the hexagons may be such that the aspect ratio of the hexagons is the same as that of the cameras used, alternatively, there may be no elongation in either the horizontal or vertical directions.
- the same hexagonal configurations of cameras may be deployed on a tubular surface to allow viewpoints that encircle the scene. If the tele-presence application does not use viewpoints from all 360°, then the video blanket need only have an angular extent equal to the angular extent of the desired viewpoints. Likewise, the linear extent of the tubular video blanket need only reach far enough to contain the desired linear viewpoints. It should be noted the linear orientation of the tubular surface may point in any direction, depending upon the specific application. Also, the tube need not be circular. In particular, coverage of an oval scene such as a stadium would be well suited to the video blanket approach. Although a non-circular tube leads to some additional calculational complexity, most of this additional complexity would take place offline while calculating input parameters describing camera placement and orientation. Therefore, video blankets on non-circular tubular surfaces may be used as easily as those on circular tubular surfaces. It is also possible to use a tubular surface with a changing perimeter, either in shape or length.
- spheroidal video blankets may not need to provide viewpoints from all possible angles and therefore may not have to cover the entire surface. Most often, spheroidal surfaces will be used when the tele-presence user desires viewpoints from at least a hemispheroidal region. For these applications the spheroidal video blanket may be composed of cameras placed at the vertices of hexagons and pentagons tiled together. Carbon atoms in fullerenes provide useful models of how to arrange the hexagons and pentagons for optimally tiling various spheroidal surfaces.
- FIG. 2 is a flowchart showing the method of an exemplary embodiment of the present invention to create high quality virtual images.
- real images are taken 101 and the viewpoint selected 102 first. Based on camera and viewpoint parameters, the system next chooses which images are to be used to create the high quality virtual image at step 103 . For each selected image a local depth map is calculated 104 . Next a calculation is performed 105 using the viewpoint parameters and information from the local depth maps to determine the warp parameters to be used to warp the real images to the selected viewpoint. The images are warped 106 to the selected viewpoint. Finally the warped images are merged 107 and provided 108 as a high quality virtual image of the scene as seen from the selected viewpoint.
- Viewpoint selection at step 102 may be made interactively by a user employing a device such as a joystick, trackball, or mouse, or may be based on criteria such as user orientation and positioning. Viewpoint selection 102 may also be predetermined to follow a set trajectory or to follow certain features in the scene, or a combination of the above methods.
- Image selection at step 103 usually selects the two or three best images based on the proximity of the real camera's position to the virtual viewpoint when the virtual viewpoint is approximately on the same surface as the cameras.
- the selection criteria may include images from cameras farther from the line of the virtual viewpoint, even cameras with orthogonal fields of view.
- the local depth map calculation at step 104 may be accomplished in a number of ways that will be apparent to one skilled in the art.
- a view-based volumetric method incorporating optical flow is used.
- a color segmentation based stereo method is used to acquire the local depth map. These methods are chosen due to their amenability to real-time computation.
- the use of depth sensing, non-visual sensors such as range finders and structured light systems has also been contemplated.
- warp parameters to warp the real images to the new viewpoint can be generated at step 105 using a depth based warping algorithm.
- the depth information derived in the reference image Before the depth information derived in the reference image can be used it is desirably converted into the new view coordinate system for rendering purposes. Because the relative pose between the reference view and the new view is known, the depth transformation can be easily derived.
- the inventors have determined that the depth, Z, of a pixel in the reference view may be expressed by equation (1).
- the image warping process 106 tends to produce the correct visibility (which part of the scene should be visible). Using techniques such as hole filling and image blending, exposed occluded image regions can be filled. Various techniques of image warping known to those skilled in the art may be used to forward warp the real images to the new viewpoint. Examples of forward warping techniques that may be used in the present invention are described in U.S. Pat. No. 5,963,213.
- new views may be synthesized by combining the warped images from these local views.
- depth information is desirably transformed into this new view as well. This is implemented by forward-warping the local depth image.
- the warped images are then merged into a high quality virtual image.
- the image merging process 107 may be performed in any standard manner known to those skilled in the art with one caveat, missing pixels in the warped images do not contribute to the final image, either as part of an average or in a filtered value. In other words, any occluded features from one image are filled exclusively by information from images in which the feature is not occluded.
- the local depth maps are used to determine a merging method optimizes the representation of that feature. Examples of image merging methods that may be used in the present invention are described in U.S. patent application Ser. No. 09/274,064, METHOD AND APPARATUS FOR REMOVING BLANK AREAS FROM REAL-TIME STABILIZED IMAGES BY INSERTING BACKGROUND INFORMATION.
- the final step 108 is to provide the high quality virtual image.
- an exemplary color segmentation method of local depth estimation is used to improve estimation of dense scene structure using a generalized stereo configuration of a pair of cameras. As is the norm in stereo vision, it is assumed that the intrinsic camera parameters and the exterior pose information are provided. Extraction of dense 3D structure involves establishing correspondence between the pair of images.
- Stereo matching has to deal with the problems of matching ambiguity, image deformations due to variations in scene structure, delineation of sharp surface boundaries, and unmatched regions due to occlusions/deocclusions in the two images.
- window operations are performed to integrate information over regions larger than a pixel. This leads to the classical matching disambiguation versus depth accuracy trade-off. In areas with sufficient detail, small windows may provide enough matching information, but matching over a larger range of depth variations (disparities) may not be possible due to ambiguous matches.
- a plane plus residual disparity representation for each color segment has been used to create an exemplary color segmentation method of local depth mapping. More specifically, in each color segment, the depth surface is modeled as a plane surface plus small depth variations for each pixel. Using this representation, the depth in textureless regions is guaranteed to be smooth. Further, a way of deriving reasonable depth estimates even for unmatched regions by hypothesizing depth of a given region based on neighboring regions may be employed.
- color segmentation is not an end goal in this method. Over-segmentation of smooth surfaces is tolerated. Exemplarary embodiments of this invention are based on the generally valid heuristic that depth boundaries coincide with color segmentation boundaries. Association of color segments with semantic/object regions need not be attempted as, in general, color segmentation works.
- a way of initializing the representation for each segment is to compute an image-disparity based local matching score. Then find the best match for each pixel in a segment and fit a plane.
- a simple recursive algorithm adjusts the plane recursively.
- a more basic global matching criterion is employed. It states that if the depth is correct, the image rendered according to the depth into the second viewpoint should be similar to the real view from that viewpoint.
- This criterion follows the paradigm of analysis by synthesis and is the ultimate matching criterion. It provides a method for checking the goodness of any given depth map by enforcing global visibility. Accurate depth boundaries and thin structures can be obtained based on this criterion too.
- a straightforward local greedy search algorithm may be used.
- all the neighboring depth hypotheses of each segment are tested while all other segments are kept fixed.
- the neighborhood depth hypothesis that gives the best global matching score is recorded.
- After all segments have been tested, their depths are updated by choosing from the initial depth and the best neighborhood hypothesis according to the matching scores. This process is performed iteratively until either the total number of segments with depth changes is small or the number of iterations exceeds a certain value.
- This process allows the correct depth to propagate because, by hypothesizing the correct depth, the warped image induces better matching.
- the depth of a background segment may be wrongfully computed as the foreground depth because of the propagation of depth from a nearby textured foreground region.
- the error can be corrected if the background segment is hypothesized to have the depth of the correct neighboring background segment and that hypothesis wins. This process has been found to tolerate large initial depth errors.
- Another benefit of the hypothesizing depths in neighborhoods is that it helps to derive reasonable depth for unmatched regions.
- the depth is more likely to be the extension of the neighboring background segment as shown in FIG. 4 .
- the drawing in FIG. 4 illustrates three segments in a reference, segment 500 in the background, segment 504 in the foreground, and segment 502 which is occluded in a second image (not shown).
- the dotted line 506 illustrates the position of segment 504 in the second image. Since segment 502 appears in only one of the image it is not possible to obtain a certain depth.
- the depth of segment may be hypothesized to be the same as the depth of segment 500 rather than left undefined. This estimate may be refined by information from additional images.
- FIG. 3 is a flowchart which illustrates the steps of this exemplary color segmentation method of creating local depth maps.
- the images are separated into color segments, step 400 .
- Any algorithm that decomposes an image into homogeneous color regions will work for that purpose.
- the most important parameter in the algorithm is the range of pixel values (or the combined range of sub-pixel values) selected as a threshold for splitting a region into multiple sub-regions. If this range is small, the image can be over-segmented. If this range is large, the image is under-segmented. Because the exemplary algorithm enforces the depth continuity inside each segment strictly, under-segmentation should be avoided.
- the method proposed in Robust Analysis of Feature Spaces: Color Image Segmentation by D. Comaniciu and P. Meer, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1997 is one such method that may be used.
- an initial depth estimate is made for each segment, step 402 in FIG. 3 .
- the three steps for the initial depth representation are (i) computing matching scores in an image-disparity volume, (ii) plane fitting in each segment, and (iii) residual disparity computation in each segment.
- the correspondence of a point in the second view lies on the same scan line as the reference view.
- the horizontal displacement of the corresponding point is called disparity.
- the matching point lies on the epipolar line in the second image.
- matching scores in an image-disparity volume are first computed. More specifically, the matching scores for all possible horizontal displacements (within a range and with a fix displacement interval) are computed first. This forms a three-dimensional matching score array, which we call image-disparity matching volume.
- Each cell (x, y, d) holds the matching score for the correlation between pixel (x, y) in the reference image and (x+d, y) in the second image. Then, for each pixel, the best score is picked and the corresponding displacement is transformed into depth.
- the same idea may be applied to arbitrary views, except that the formulation is more complicated. In both cases, the isodisparity surface is a frontal plane in the reference view.
- each row of A is the [x, y, 1] vector for a pixel and each row of B is its corresponding 1/Z.
- FIGS. 5 a – 5 c An iterative fitting process may be adopted to reduce the effect of outliers. This idea is illustrated in FIGS. 5 a – 5 c .
- the depth of every pixel in the image is decided by picking the best matching score.
- Matching scores may be calculated by a number of measures such as normalized correlation matching (or sum of absolute difference) score of a gray level or color window around the point, similarity in motion between neighboring pixels etc.
- Different approaches for checking for alignment quality are described in a U.S. patent application Ser. No. 09/384,118, METHOD AND APPARATUS FOR PROCESSING IMAGES by K. Hanna, R. Kumar, J. Bergen, J. Lubin, H. Sawhney.
- plane 600 is fitted in a segment.
- the depth of each pixel is chosen within a given range 608 of the fitted plane by finding the best matching score in that range.
- FIG. 5 b outlying pixels 604 and 606 have been changed to fit within range 608 .
- the plane parameters are updated accordingly based on these depths.
- FIG. 5 c illustrates new plane 602 and new range 610 . This process iterates several times until the plane parameters do not change significantly. This process is particularly useful for fitting planes in large textureless regions where matching ambiguities occurs. More generally, any other robust method of plane fitting like M-estimation, least median squares or RANSAC may be employed.
- Z p is known.
- Z r is computed by locating the best match in the image-disparity volume within a small range of Z p . Residual disparity Z r is smoothed in each segment to obtain the initial color segmentation based depth representation.
- step 404 is to create a number of depth hypotheses for each color segment.
- a hypothesis for each segment is generated from each neighboring segment.
- the plane parameters of a given segment are replaced using those of a neighboring segment to create the hypothesis.
- residual disparity for each pixel is found by searching around the plane and smoothing within the given segment.
- the depth hypotheses of a single segment are then tested while all the other segments maintain the initial depth, step 406 .
- the depth representations are updated after testing is done for all segments. Since only the depth of one segment is changed each time, only a small portion of the image needs to be tested.
- An exemplary algorithm has been developed which takes advantage of this fact. This algorithm is illustrated in FIGS. 6 a – 6 d .
- the reference image, FIG. 6 a is first warped to the second view using the initial depth (i.e. initial depth hypothesis for each segment). We call this image the base warp, FIG. 6 b.
- the depth of segment 700 is replaced by one of its neighborhood hypothesis, to compute its matching measure, we only need to consider those pixels affected by the depth change. For example, in FIG. 6 c , the depth of segment 700 is changed. In the warped image, FIG. 6 d , region 706 of segment 702 becomes visible while segment 700 becomes invisible.
- the matching score of the new depth map is computed by adding matching score of region 706 to the base warp score and subtracting matching score of segment 700 . This example suggests an exemplary algorithm for testing these hypotheses.
- the warped depths, the segmentation ID's, and the matching scores of the two top-most layers are stored. Changes in the matching scores over base warp are computed by adding the matching scores of pixels that become visible and subtracting scores of pixels that become invisible. Since for each test, only the depth of one segment is changed, only the two top-most layers may become visible and information regarding those two layers should be recorded. The third layer will be blocked by at least one of the two layers originally in front of it and always invisible, therefore it does not affect the matching score.
- step 408 is to update the depth of each segment using the hypothesis with the best positive improvement. If none of the hypotheses gives positive improvement, keep the initial depth for that segment.
- step 410 it is determined if the process of improving the local depth maps has reached a predetermined maximum number of iterations. If this number has been reached the present depth map is selected as the final local depth map, step 414 . If the number has not been reached, the most recent set of depth changes is analyzed, step 412 . If the changes are less than a predetermined criterion, then the present depth map is selected as the final local depth map, step 414 . Otherwise, the process returns to step 404 and another iteration of depth hypotheses begun. It should be noted that the exemplary ordering of steps 410 and 412 has been selected, since step 410 is less calculationally demanding, but these steps may be reversed, or either one may be omitted.
- the plane plus small residual disparity representation may not be sufficient for objects with highly curved surfaces. It has been contemplated that more flexible depth representations may solve this problem. Also, it may occasionally occur that depth boundaries appear in homogeneous color segments. A method of hypothesizing splits in problematic segments, or intentional over-segmentation, may be used to overcome these issues.
- color segmentation may be used for optical flow computation, too.
- depth based forward warping By replacing the depth based forward warping with occlusion compatible traversal warping these methods may be combined.
- certain information such as positions of epipoles is roughly known, this may prove an advantageous approach.
- FIG. 1 For a 2D image, the total searching space is three-dimensional. This space can be parameterized using image coordinates and disparity and is called a scene volume.
- the view-based volumetric approach generates this volume by computing the goodness of the match for all disparity values of all pixels in the reference images.
- One possible solution can be achieved based on the disparity value with the maximum matching score for each pixel. Due to image noise, however, and matching ambiguity, the solution may be noisy or totally wrong.
- Various physical constraints of the scene can be imposed to regularize the solution. Computationally, these constraints are conveniently formulated as a relaxation process in the 3D volume.
- R n ⁇ ( x , y , d ) 1 max ( x ′ , y ′ , d ′ ) ⁇ ⁇ ⁇ S n ⁇ ( x ′ , y ′ , d ′ ) ( 3 )
- the parameter ⁇ controls the strength of uniqueness constraint.
- ⁇ 3.
- parallax describes the amount of displacement in the inspection image along the epipolar line and can be viewed as generalized disparity.
- Parallax is a relative affine structure invariant to the pose of the inspection view. It provides the mechanism for integrating more than two views.
- a simple splatting based forward warping algorithm may be used to obtain pixel values in the warped images, but an algorithm of this type may cause blurred images or holes by using too large or too small a splatting kernel.
- the forward warping algorithm proposed by W. R. Mark and G. Bishop in their article entitled “Efficient Reconstruction Techniques for Post-Rendering 3D Image Warping” (UNC Computer Science Technical Report TR98-011, University of North Carolina, Mar. 21, 1998) may be used in an exemplary embodiment of the present invention.
- This algorithm solves the problem of image distortion by splatting pixels according to a mesh based internal representation. Depth discontinuity is detected as the stretch of the mesh exceeds a certain threshold.
- FIG. 8 illustrates the process used in the exemplary embodiment of the invention.
- the original reference image 60 and the corresponding flow 61 are first super sampled to higher resolution. Then, for each pixel 62 at that resolution, the quadrilateral 63 that surrounds it is found by averaging the position of its neighboring pixels 62 . The value of the original pixel is then splatted into this quadrilateral area 63 . Finally, the super-resolution image is down-sampled to the original size.
- Z-buffering is a rendering technique that is used to ensure correct visibility of objects in the rendered images. Z-buffering may be used in the present invention, but the computational demands make it a less than ideal choice for real-time applications.
- Another more efficient technique, used in an exemplary embodiment of the present invention and illustrated in FIGS. 7 a – 7 d is to warp pixels according to the occlusion compatible traversal order 41 .
- the ideal traversal order 41 is to move along a direction perpendicular to the epipolar lines, and concurrently move toward or away from the epipole 40 using the epipole 40 as the focus of contraction or the focus of expansion.
- FIG. 7 a the ideal traversal order 41 is to move along a direction perpendicular to the epipolar lines, and concurrently move toward or away from the epipole 40 using the epipole 40 as the focus of contraction or the focus of expansion.
- FIGS. 7 c and 7 d illustrate these traversal orders, respectively, for the cases in which the epipoles are foci of contraction. This approximation works well even when the epipole 40 is outside the image.
- FIG. 9 is a top-plan view of a space to be imaged showing multiple cameras 71 being used to capture images of a scene 70 from a variety of angles.
- the cameras 71 are arranged in a video blanket configuration such as that shown in FIG. 1 .
- FIG. 10 is a block diagram of the virtual camera system 80 of the present invention.
- At least two fixed cameras 81 are used to capture real images. Parameters specifying camera positions, orientations, and resolution can be calculated offline. In an exemplary embodiment of the present invention these cameras 81 are arranged in a video blanket configuration.
- the viewpoint selection input 82 specifies the position, orientation, and zoom parameters of the high quality virtual image to be created, and also provides control over the image selection means 83 . Based on camera and viewpoint parameters, the image selection means 83 next chooses at least two images that will be used to create the high quality virtual image. For each selected image a local depth map is calculated by a depth estimation means 84 .
- a calculation means 85 performs the calculation of the warp parameters necessary to warp the real images to the selected viewpoint. Camera and viewpoint parameters as well as information from the local depth maps are used. The warp parameters are then used by the image warper 86 to warp the real images to the selected viewpoint.
- the processes of depth estimation, warp parameter calculation, and image warping may be carried out serially, or as shown FIG. 10 as a pipelined parallel process.
- An exemplary embodiment of the present invention uses parallel processing in all three of these elements 84 , 85 , and 86 .
- an image merger 87 merges the warped images into a high quality virtual image of the scene as seen from the selected viewpoint.
- the output 88 may be at least one video monitor, video recording device, computer, broadcast system, or combination.
- the virtual camera system 80 is operated using the method described above with reference to FIG. 3 .
- FIG. 11 is a series of drawings demonstrating the steps of image processing employed in the present invention to create a high quality virtual image 93 , from two selected real images 90 and 190 .
- the scene depicted contains a wall and a cube 94 , which is suspended in front of the wall.
- Image 90 shows cube 94 from slightly above and to the right, while image 190 shows cube 94 from slightly above and to the left.
- image 91 shows cube 94 from slightly above and to the left.
- cube 94 is seen from slightly above and straight on.
- First depth maps 91 and 191 are created by step 96 .
- the cube 94 is shown to lie at various depths while the wall has a uniform depth.
- Next warped images 92 and 192 showing the cube 94 from slightly above and straight on are generated in step 97 .
- An occluded region 95 appears in each of the warped images 92 and 192 .
- These occluded regions 95 are portions of the wall which had been occluded by the cube 94 in image 90 and 190 .
- the warped images 92 and 192 are merged in step 98 to create the high quality virtual image 93 . In this image the occluded region 95 from image 92 has been filled by using information for that region from warped image 192 .
- the system identifies these occluded regions using differences in the depth map between the two images. Likewise, the occluded region 95 from image 192 has been filled by using information for that region from warped image 92 . In this way, this exemplary method of the present invention creates the high quality virtual image without any missing regions.
- the present invention demonstrates the feasibility of providing view coverage based on a sparse collection of cameras.
- the approach relies on local depth sensing, global pose estimation and image-based rendering. Because all the computations involve a local collection of cameras, the algorithms can be easily mapped to hardware and real-time implementations. Therefore, the inventors are able to use video based depth sensing with appropriate imaging parameters, camera configurations and the associated algorithms, to provide a flexible, versatile and cost effective solution for the immersive tele-presence systems.
Abstract
Description
Z p=1/Z=ax+by+c (2)
A[a, b, c]t=B (3)
1/Z=Z p +Z r (4)
-
- where Φ CD represents the cooperative zone, and e(x,y,x′y′) is the similarity function for pixels (x,y) and (x′,y′) in the reference Image. To reinforce the uniqueness constraint, the inhibition, Ra(x, y, d), for voxel (x,y,d) is given by equation (3)
-
- where Θ is the inhibition zone. Overall, the updating formula is for voxel (x, y, d) is given by equation (4)
L(x,y)=(1−α)C(x,y)+αexp{−SSD(x,y)/σ2} (5)
-
- is weighting factor for SSD. In an exemplary embodiment, α=0.5.
Claims (15)
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JP2002547092A JP2004525437A (en) | 2000-12-01 | 2001-12-03 | Method and apparatus for synthesizing a new video and / or still image from a group of actual video and / or still images |
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EP1337961A4 (en) | 2007-03-14 |
JP2004525437A (en) | 2004-08-19 |
WO2002045001A1 (en) | 2002-06-06 |
US20020061131A1 (en) | 2002-05-23 |
EP1337961A1 (en) | 2003-08-27 |
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